Mass spectrometric methods for the analysis of protein structures are being improved, applied and tested in independent and collaborative research projects in proteomics. Projects in separation methods, derivatization chemistry, and data processing are in progress. [unreadable] [unreadable] Peptide analyses by LC-MS/MS generate large datasets, imposing labor-intensive efforts to consolidate peptide and/or protein identification information into meaningful knowledge. Software bioinformatics tools can facilitate and accelerate high-throughput peptide data analyses. Toward this goal, we have continued refining and testing an integrated workflow designed to maximize the amount of peptide sequence information from LC-MS/MS data. This strategy integrates several independent data search and data mining technologies using a relational database architecture. An iterative search tool acquires results from database search algorithms; spectral filtering and de novo sequencing algorithms are integrated to maximize spectral assignment. The resulting open source software tools will provide an automated data processing/analysis environment for researchers. We have tested and refined features previously added to an open source software package DBParser to combine and validate the search results from OMSSA, Mascot, and X!Tandem. DBParser produces combined or compared peptide or protein lists for subsequent evaluation by research scientists. Protein summaries are based upon the principle of parsimony, identifying the minimal set of proteins that can explain all of the observed peptides. DBParser's value for generating quantitative data and parsimonious analyses of proteins associated with experiments has led to its use for analyzing larger datasets than initially anticipated (100?s of data files with millions of spectra). Second generation software, MassSieve, is planned to be a scalable, multi-platform compatible tool for sorting, comparing and analyzing peptide data with many of the functions of DBParser. The need to merge and compare data sets from multiple experiments necessitates software that is inherently scalable. MassSieve will be a complete rewrite of the DBParser code based upon lessons learned from the previous version. The main intent of the redesign is improve the speed of the algorithm to facilitate a more interactive user experience as well as allow the processing of larger datasets produced by the faster scanning LTQ vs. the older LCQ instruments. In addition, the design of the code will be more modular to ease future modification and to allow others to integrate the program into their own laboratory computational environment. [unreadable] [unreadable] After determining the identity of a protein in a mixture by mass spectrometry, quantity is the most important experimental variable required in biological experiments. We are developing procedures to use isotope labeled fluorescent fusion proteins for the establishment of accurate and absolute quantification of tryptic peptides. Quantitative measurement of peptides is dependent upon proteolytic yield as well as ionization response factors. Isotope labeled fluorescent fusion proteins extend the capability of quantitative measurement to all proteins, facilitating accurate stoichiometric assignments that can be used for structural modeling with cryo-electron microscope or other imaging data. We are adapting and extending the use of isotope labeled fluorescent fusion proteins to determine the stoichiometry of proteins in cellular organelles.[unreadable] [unreadable] Laser Induced Fluorescence Detection (LIFD) is an alternative choice to mass spectrometric techniques for a quantitative proteomic measurements due to its high sensitivity. We designed, built and evaluated a microscale HPLC system with a fluorescence detection coupled to an ESI mass spectrometer. The results indicate that LIFD with NDA (Naphthalene DicarboxAldehyde) derivatization is highly accurate as a quantitative technique and is equivalent in sensitivity to mass spectrometry. Native tryptophan fluorescence, while less sensitive than NDA labeling, exhibits good inherent detection properties owing to the absence of interfering fluorescence in underivatized peptides.[unreadable] [unreadable] The ribosome is the universal macromolecular machine involved in translating mRNA transcript into polypeptides. It has been extensively studied and characterized by structural biologists. It is an accessible and renewable source of a model sub-cellular protein complex. As such, it is a reference material for the assessment and evaluation of analytical and bioinformatic tools under development, and is being used in several analytical protocols in LNT. We propose to answer basic biochemical question arising from prior observations that reported the detection and chemical characterization of ?-methylthioaspartic acid at position 88 in E. coli ribosomal protein S12. The location of this unusual posttranslational modification in S12 and its orthologs from other bacteria is highly conserved phylogenetically. Beta-methylthioaspartic acid occurs in a region of S12 that is a mutational hotspot resulting in both antibiotic-resistant and antibiotic-sensitive phenotypes. In bacteria, S12 binds to 16S rRNA in regions associated with the fidelity of codon recognition. When coupled with the parsimonious nature of bacterial genetics, it is likely that ?-methylthioaspartic acid is both structurally and functionally important. A goal of the proposed research is to determine the biological function of ?-methylthioaspartic acid by elucidating the enzymology of modification. Experiments with an Orbitrap mass spectrometer will be directed toward optimizing instrument control language to promtoe high energy collisions in the C-trap, potentially affording the ability to fragment much larger peptides produced from selected proteolytic cleavages and intact analysis of proteins below 20kDa.[unreadable] [unreadable] J. Kowalak chaired a Proteomics Standards Research Group (sPRG) formed by the Association of Biomolecular Resource Facilities (ABRF) to respond to the need of standards in proteomics, The sPRG sought to produce a reasonably complex, well-defined mixture of proteins to serve as a reference standard. Forty nine human proteins, either purified from their natural source or recombinantly expressed, were selected based on criterion of purity (>95%) as assessed by 1D SDS electrophoresis, isoelectric focusing, and reversed phase HPLC, and quantified in triplicate by amino acid analysis. Five picomole aliquots of each protein were combined into a single vial and lyophilized. Approximately 130 samples were distributed to laboratories world-wide and 78 data sets were returned. The results spanned a wide range of performance, from 48/49 to 0/49 correct (mean = 32, median =35), with two important outcomes. First, a significant number of respondents reported only high confidence protein identifications and did not report any false positives. Second, no single form of instrumentation or technology excelled consistently. Success was apparently more a function of operator skill and ability to maximize the performance variables of available instrumentation. The results of this study have been the subject of presentations at several national meetings (ABRF-Long Beach, USHUPO-Boston, ASMS-Seattle) and will be published in a peer-reviewed journal. A significant value of this study to the proteomics community is continued access to a proteomics standard that can be used to objectively evaluate techniques and laboratories.